12. Biostatistics

Question 1

Define “parametric tests” and give examples

Answer 1

Interval or ratio data; tests hypothesis in NORMALLY distributed population. MORE POWER.

Question 2

Examples of parametric tests

Answer 2

t-test; ANOVA; Pearson Coefficient; Multiple Regression

Question 3

Define “non-parametric tests” and give examples

Answer 3

Data from a SKEWED distribution; LESS POWER

Question 4

Examples of non-parametric tests

Answer 4

Chi-Square; Logistic regression; Spearman Rho; Wilcoxon Signed Rank

Question 5

Define “degrees of freedom”

Answer 5

of pieces of info that can vary independently from one another; n -1, where n is # of cases in the sample

Question 6

One-tailed t-test

Answer 6

Used when direction of difference is known or postulated

Question 7

Two-tailed t-test

Answer 7

Used when direction of difference is unknown or not postulated

Question 8

Paired t-test

Answer 8

BEFORE & AFTER tests of SAME GROUP; Pre & Post test analysis

Question 9

Independent t-test

Answer 9

Most common statistical method; Simple (yet powerful); Comparison between control and test group. ONLY TWO GROUPS CAN BE COMPARED. Assumes normal distribution. AKA = two-sample t-test; tests DIFFERENT GROUPS

Question 10

Analysis of Variance (ANOVA)

Answer 10

Accepted method of comparing TWO OR MORE groups from ONE STUDY; determines RELATIONSHIP BETWEEN IV & DV; Test statistic is an F RATIO. Variables are factors.

Question 11

Post-hoc tests

Answer 11

Defines which of 3 or more groups are actually different; test after ANOVA to detemine what the significance actually is

Question 12

Tukey

Answer 12

Post-hoc test; Used when larger number of comparisons are made; assumes groups are of equal size; Used to determine which means are significantly different

Question 13

Dunn

Answer 13

Post-hoc test; Used when only a few comparisons are made

Question 14

Bonferroni

Answer 14

Post-hoc test; Used when 5 or less comparisons are made

Question 15

Repeated Measures ANOVA

Answer 15

Used when experiment involves matched subjects; Measure an outcome in each subject before, during, and after intervention

Question 16

Analysis of Covariance (ANCOVA)

Answer 16

Enables testing of differences among at least 3 groups, while adjusting for effects of covariates or confounders on DV

Question 17

MANOVA

Answer 17

Used when 2 DVs are assessed

Question 18

Chi-Square Test

Answer 18

Non-parametric; applied to non-normally distributed populations; “Goodness of Fit test”or “Pearson Chi Square”; Statistically significant difference between observed (actual) frequencies and expected frequency of variables; Answers research questions about RATES, PROPORTIONS, OR FREQUENCIES. GREATER CHI SQUARE, LESS LIKELY DIFFERENCE IS D/T CHANCE

Question 19

Fisher’s Exact Test

Answer 19

Non-parametric test for nominal data. Used instead of Chi Square in VERY SMALL SAMPLE SIZE

Question 20

McNemar’s Test

Answer 20

Non-parametric test for nominal data from PAIRED SAMPLES

Question 21

Mantel-Haenszel

Answer 21

Non-parametric test for nominal data to control for effects of a confounder.

Question 22

Mann-Whitney U Test

Answer 22

Non-parametric test for ordinal variables; OFTEN USED TO COMPARE SURVIVAL CURVES

Question 23

Wilcoxon Rank Signed Test

Answer 23

Non-parametric equivalent of PAIRED T-TEST for ORDINAL variables. Use with 2 correlated samples (before & after) or difference between 2 groups.

Question 24

Kruskal-Wallis test

Answer 24

Non-parametric equivalent of ANOVA to compare ? 3 groups of different groups with ordinal data

Question 25

Friedman’s test

Answer 25

Non-parametric equivalent of REPEATED MEASURES ANOVA to compare ? 3 groups of RELATED groups with ordinal data

Question 26

Correlation

Answer 26

Quantitative way of measuring the strength of a relationship between two variables; DOES NOT ASSUME CAUSE & EFFECT. 0 = NO RELATIONSHIP. +1 = DEFINITE POSITIVE CORRELATION; -1 = DEFINITE INVERSE RELATIONSHIP

Question 27

Pearson’s Correlation Coefficient

Answer 27

Parametric measure of association

Question 28

Spearman’s Rho Coefficient

Answer 28

Nonparametric measure of correlation between 2 quantitative ORDINAL variables

Question 29

Regression

Answer 29

Prediction of one variable from another; Assumes cause & effect relationship. Y = mX + b

Question 30

Multiple Regression

Answer 30

Same as simple regression, but INCLUDES MULTIPLE INDEPENDENT VARIABLES and 1 DEPENDENT VARIABLE. Predictor values (IV) to predict as single DV (criterion variable)

Question 31

Logistic Regression

Answer 31

Similar to multiple regression. DV is categorical (ordinal). SHOULD NOT INCLUDE CONTINUOUS VARIABLES.

Question 32

Cronbach Alpha

Answer 32

Index of internal consistency, reliability. Degree to which responses are consistent across multiple measures of same construct.

Question 33

Homogeneity tests

Answer 33

Assumption variances of population being compared with t-test or ANOVA are equal. Assumes equal populations. Possible violations detected with LEVINE’S TEST.

Question 34

Levine’s test

Answer 34

Used prior to conducting ANOVA and before interpretting results of t-tests

Question 35

Limitations

Answer 35

Influences researcher CANNOT CONTROL. Potential weakness of study.

Question 36

Delimitations

Answer 36

Influences that could be controlled, but weren’t.

Question 37

Bias

Answer 37

Preference toward a result. May be d/t SAMPLE selection; who is reading the results; BLINDED STUDIES